Sanfilippo Filippo, La Via Luigi, Dezio Veronica, Santonocito Cristina, Amelio Paolo, Genoese Giulio, Astuto Marinella, Noto Alberto
Department of Anaesthesia and Intensive Care, A.O.U. Policlinico-San Marco, site "Policlinico G. Rodolico", Via S. Sofia N 78, 95123, Catania, Italy.
School of Anaesthesia and Intensive Care, University Hospital "G. Rodolico", University of Catania, 95123, Catania, Italy.
Intensive Care Med Exp. 2023 Apr 3;11(1):15. doi: 10.1186/s40635-023-00505-7.
Assessment of the inferior vena cava (IVC) respiratory variation may be clinically useful for the estimation of fluid-responsiveness and venous congestion; however, imaging from subcostal (SC, sagittal) region is not always feasible. It is unclear if coronal trans-hepatic (TH) IVC imaging provides interchangeable results. The use of artificial intelligence (AI) with automated border tracking may be helpful as part of point-of-care ultrasound but it needs validation.
Prospective observational study conducted in spontaneously breathing healthy volunteers with assessment of IVC collapsibility (IVCc) in SC and TH imaging, with measures taken in M-mode or with AI software. We calculated mean bias and limits of agreement (LoA), and the intra-class correlation (ICC) coefficient with their 95% confidence intervals.
Sixty volunteers were included; IVC was not visualized in five of them (n = 2, both SC and TH windows, 3.3%; n = 3 in TH approach, 5%). Compared with M-mode, AI showed good accuracy both for SC (IVCc: bias - 0.7%, LoA [- 24.9; 23.6]) and TH approach (IVCc: bias 3.7%, LoA [- 14.9; 22.3]). The ICC coefficients showed moderate reliability: 0.57 [0.36; 0.73] in SC, and 0.72 [0.55; 0.83] in TH. Comparing anatomical sites (SC vs TH), results produced by M-mode were not interchangeable (IVCc: bias 13.9%, LoA [- 18.1; 45.8]). When this evaluation was performed with AI, such difference became smaller: IVCc bias 7.7%, LoA [- 19.2; 34.6]. The correlation between SC and TH assessments was poor for M-mode (ICC = 0.08 [- 0.18; 0.34]) while moderate for AI (ICC = 0.69 [0.52; 0.81]).
The use of AI shows good accuracy when compared with the traditional M-mode IVC assessment, both for SC and TH imaging. Although AI reduces differences between sagittal and coronal IVC measurements, results from these sites are not interchangeable.
评估下腔静脉（IVC）呼吸变异对于估计液体反应性和静脉充血可能具有临床意义；然而，从肋下（SC，矢状面）区域进行成像并不总是可行的。目前尚不清楚冠状面经肝（TH）IVC成像是否能提供可互换的结果。在床旁超声检查中使用具有自动边界跟踪功能的人工智能（AI）可能会有所帮助，但需要进行验证。
对自主呼吸的健康志愿者进行前瞻性观察研究，评估SC和TH成像中的IVC可塌陷性（IVCc），采用M型测量或使用AI软件进行测量。我们计算了平均偏差和一致性界限（LoA），以及组内相关系数（ICC）及其95%置信区间。
纳入60名志愿者；其中5人未观察到IVC（n = 2，SC和TH窗口均未观察到，3.3%；n = 3在TH方法中未观察到，5%）。与M型相比，AI在SC（IVCc：偏差 - 0.7%，LoA [- 24.9；23.6]）和TH方法（IVCc：偏差3.7%，LoA [- 14.9；22.3]）中均显示出良好的准确性。ICC系数显示出中等可靠性：SC为0.57 [0.36；0.73]，TH为0.72 [0.55；0.83]。比较解剖部位（SC与TH），M型产生的结果不可互换（IVCc：偏差13.9%，LoA [- 18.1；45.8]）。当使用AI进行此评估时，这种差异变小：IVCc偏差7.7%，LoA [- 19.2；34.6]。M型的SC和TH评估之间的相关性较差（ICC = 0.08 [- 0.18；0.34]），而AI的相关性中等（ICC = 0.69 [0.52；0.81]）。
与传统的M型IVC评估相比，AI在SC和TH成像中均显示出良好的准确性。虽然AI减少了矢状面和冠状面IVC测量之间的差异，但这些部位的结果不可互换。
